Global Analysis: Youth Education Completion and Economic Indicators
Global Progress in Youth Education and Economic Development
Analyzing UNICEF and World Bank Data (2024)
Prepared by Ajaatha Shathru Ilangovan
Youth Education and Development: A Global Story
1. Introduction
Understanding the well-being of young populations requires examining both education and economic progress. In this report, we merge UNICEF’s education completion data with World Bank development indicators to tell a broader story:
How does economic strength influence education?
Where are the major gaps?
Are health outcomes associated with educational achievement?
How does global education inequality manifest across income groups?
By connecting education to economic and health conditions, we reveal patterns critical for sustainable global development.
2. Data Preparation
Code
import pandas as pdimport plotly.express as pxfrom plotnine import*import pycountryfrom statsmodels.nonparametric.smoothers_lowess import lowess# Load the merged datadf = pd.read_csv("UNICEF-Merged Data.csv")# Clean the datadata = df.dropna(subset=['obs_value'])# Create ISO Alpha-3 codes for countriesdef get_alpha_3(country_name):try:return pycountry.countries.lookup(country_name).alpha_3exceptLookupError:returnNonedata['iso_alpha3'] = data['country'].apply(get_alpha_3)data = data.dropna(subset=['iso_alpha3'])# GDP Group categorizationdef gdp_group(gdp):if pd.isna(gdp):return'Unknown'elif gdp >12000:return'High Income'elif gdp >4000:return'Upper-Middle Income'elif gdp >1000:return'Lower-Middle Income'else:return'Low Income'data['GDP_Group'] = data['GDP per capita (constant 2015 US$)'].apply(gdp_group)
Observation: The world map highlights significant disparities between countries. Wealthier regions show higher completion rates while many lower-income regions lag behind.
Observation: Despite fluctuations, the overall global education completion trend shows consistent improvement.
3.6 Bubble Chart: Education Completion vs Life Expectancy
Code
bubble_data = data.dropna(subset=['Life expectancy at birth, total (years)', 'Population, total']).copy()def assign_region(country):if country in ['United States', 'Canada', 'Mexico', 'Brazil', 'Argentina', 'Chile']:return'Americas'elif country in ['Germany', 'France', 'United Kingdom', 'Italy', 'Spain']:return'Europe'elif country in ['China', 'India', 'Japan', 'Indonesia', 'Vietnam']:return'Asia'elif country in ['South Africa', 'Nigeria', 'Egypt', 'Kenya']:return'Africa'else:return'Other'bubble_data['region'] = bubble_data['country'].apply(assign_region)fig = px.scatter( bubble_data, x="obs_value", y="Life expectancy at birth, total (years)", size="Population, total", color="region", hover_name="country", size_max=60, title="Education Completion vs Life Expectancy (Bubble Size = Population, Colored by Region)")lowess_smoothed = lowess(bubble_data['Life expectancy at birth, total (years)'], bubble_data['obs_value'], frac=0.4)fig.add_scatter( x=lowess_smoothed[:, 0], y=lowess_smoothed[:, 1], mode='lines', line=dict(color='black', dash='dash'), name='Reference Trend Line')fig.update_layout( xaxis_title="Education Completion Rate (%)", yaxis_title="Life Expectancy (Years)", legend_title="Region", template="plotly_white")fig.show()
Observation: Higher education completion rates are associated with longer life expectancy, likely due to indirect factors such as improved economic conditions, better healthcare access, increased health awareness, and enhanced social support systems.
3.7 Box Plot: Education Completion by GDP Group
Code
def gdp_group(gdp):if pd.isna(gdp):return'Unknown'elif gdp >12000:return'High Income'elif gdp >4000:return'Upper-Middle Income'elif gdp >1000:return'Lower-Middle Income'else:return'Low Income'data['GDP_Group'] = data['GDP per capita (constant 2015 US$)'].apply(gdp_group)# Step 2: Clean data and reorder GDP_Groupbox_data = data[data['GDP_Group'] !='Unknown'].copy()box_data['GDP_Group'] = pd.Categorical( box_data['GDP_Group'], categories=['Low Income', 'Lower-Middle Income', 'Upper-Middle Income', 'High Income'], ordered=True)# Step 3: Create box plot( ggplot(box_data, aes(x='GDP_Group', y='obs_value', fill='GDP_Group')) + geom_boxplot() + theme_minimal() + labs( title='Distribution of Education Completion by Economic Group', x='GDP Income Group', y='Education Completion Rate (%)' ) + theme(legend_position='none'))
Insight: Higher income groups exhibit higher median education completion rates with lower spread compared to lower-income groups.
4. Conclusion
Key Takeaways:
Higher education completion rates are often associated with improved economic prosperity and longer life expectancy, reflecting deeper societal advancements such as healthcare access, workforce development, and informed civic participation.
Significant regional and economic disparities persist, with low- and middle-income countries facing structural barriers to achieving similar education outcomes.
Continuous investment in education infrastructure, inclusive policies, and social development initiatives remains critical to bridging these gaps and driving progress towards the Sustainable Development Goals (SDGs).
Education is both a cause and a result of development. It fuels economic growth, health improvements, and social mobility, while advancements in a nation’s economy, healthcare, and governance systems create more equitable access to education, reinforcing a continuous cycle of progress.